ABSTRACT
In solid propellant rocket propulsion, the design of the propellant grain is a decisive
aspect. The grain design governs the entire motor performance and, hence, the
whole rocket mission. The ability to decide, during design phase, the proper grain
design that satisfies the predefined rocket mission with minimum losses is the
ultimate goal of solid propulsion experts. This study enables to predict the pressure
time curve of rocket motor with star grain configuration and also to optimize the
performance prediction tool through optimization methods to maximize its prediction
efficiency. A hybrid optimization technique is used. Genetic Algorithm (GA) is first
implemented to find the global optimum followed by Simulated Annealing (SA)
optimization method to find the accurate local optimum. A program for predicting the
pressure time curve of the rocket motor is created on MATLAB and then linked to GA
- SA optimizers as an application on a case study. The purposed approach is
validated against satisfying data. It is found that the developed optimized program is
capable of predicting rocket motor performance (including the effect of erosive
burning) with acceptable accuracy for preliminary design purposes.